''' @Author: xxxmy @Github: github.com/VectXmy @Date: 2019-09-26 @Email: [email protected] ''' from model.fcos import FCOSDetector import torch from dataloader.VOC_dataset import VOCDataset import math, time from torch.utils.tensorboard import SummaryWriter train_dataset = VOCDataset( "D:\\Research\\My_tamper_detect_dataset_train_val_test\\voc_dataset_tmp", resize_size=[512, 800], split='train') val_dataset = VOCDataset( "D:\\Research\\My_tamper_detect_dataset_train_val_test\\voc_dataset_tmp", resize_size=[512, 800], split='val') model = FCOSDetector(mode="training").cuda() # model.load_state_dict(torch.load("./checkpoints/voc_512x800_loss2.0635.pth")) optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) BATCH_SIZE = 3 EPOCHS = 25 WARMPUP_STEPS_RATIO = 0.12 # train_loader=torch.utils.data.DataLoader(train_dataset,batch_size=BATCH_SIZE,shuffle=True,collate_fn=train_dataset.collate_fn) # val_loader=torch.utils.data.DataLoader(val_dataset,batch_size=BATCH_SIZE,shuffle=True,collate_fn=val_dataset.collate_fn) train_loader = torch.utils.data.DataLoader(train_dataset,
# compute recall and precision
recall = tp / total_gts
precision = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = _compute_ap(recall, precision)
all_ap[label] = ap
# print(recall, precision)
return all_ap
if __name__ == "__main__":
from model.fcos import FCOSDetector
from demo import convertSyncBNtoBN
from dataloader.VOC_dataset import VOCDataset
eval_dataset = VOCDataset(cfg.dataset_dir,
resize_size=cfg.image_scale,
split='val')
print("INFO===>eval dataset has %d imgs" % len(eval_dataset))
eval_loader = torch.utils.data.DataLoader(
eval_dataset,
batch_size=1,
shuffle=False,
collate_fn=eval_dataset.collate_fn)
model = FCOSDetector(mode="inference")
model.load_state_dict(
torch.load("voc2012_512x800_epoch61_loss0.7096.pth",
map_location=torch.device('cpu')))
model = model.cuda().eval()
print("===>success loading model")
gt_boxes = []
# compute recall and precision
recall = tp / total_gts
precision = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = _compute_ap(recall, precision)
all_ap[label] = ap
# print(recall, precision)
return all_ap
if __name__ == "__main__":
from model.fcos import FCOSDetector
from demo import convertSyncBNtoBN
from dataloader.VOC_dataset import VOCDataset
eval_dataset = VOCDataset(
"D:\\Research\\My_tamper_detect_dataset_train_val_test\\voc_dataset_tmp",
resize_size=[800, 1024],
split='test')
print("INFO===>eval dataset has %d imgs" % len(eval_dataset))
eval_loader = torch.utils.data.DataLoader(
eval_dataset,
batch_size=1,
shuffle=False,
collate_fn=eval_dataset.collate_fn)
model = FCOSDetector(mode="inference")
# model=torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
# print("INFO===>success convert BN to SyncBN")
## model.load_state_dict(torch.load("./logs/voc20172012_multigpu_800x1024_epoch27_loss0.5987.pth",map_location=torch.device('cpu')))
model.load_state_dict(
torch.load(
"./checkPoints/checkPointsTmp/voc2012_512x800_epoch25_loss0.9422.pth",
''' @Author: xxxmy @Github: github.com/VectXmy @Date: 2019-09-26 @Email: [email protected] ''' from model.fcos import FCOSDetector import torch from dataloader.VOC_dataset import VOCDataset import math,time from torch.utils.tensorboard import SummaryWriter train_dataset=VOCDataset("/home/xht/voc2012/VOCdevkit/VOC2012",resize_size=[512,800],split='train') val_dataset=VOCDataset("/home/xht/voc2012/VOCdevkit/VOC2012",resize_size=[512,800],split='val') model=FCOSDetector(mode="training").cuda() model.load_state_dict(torch.load("./checkpoints/voc_512x800_loss2.0635.pth")) optimizer=torch.optim.Adam(model.parameters(),lr=1e-4) BATCH_SIZE=6 EPOCHS=30 WARMPUP_STEPS_RATIO=0.12 train_loader=torch.utils.data.DataLoader(train_dataset,batch_size=BATCH_SIZE,shuffle=True,collate_fn=train_dataset.collate_fn) val_loader=torch.utils.data.DataLoader(val_dataset,batch_size=BATCH_SIZE,shuffle=True,collate_fn=val_dataset.collate_fn) steps_per_epoch=len(train_dataset)//BATCH_SIZE TOTAL_STEPS=steps_per_epoch*EPOCHS WARMPUP_STEPS=TOTAL_STEPS*WARMPUP_STEPS_RATIO GLOBAL_STEPS=1 LR_INIT=5e-5 LR_END=1e-6
# compute recall and precision
recall = tp / total_gts
precision = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = _compute_ap(recall, precision)
all_ap[label] = ap
# print(recall, precision)
return all_ap
if __name__ == "__main__":
from model.fcos import FCOSDetector
from demo import convertSyncBNtoBN
from dataloader.VOC_dataset import VOCDataset
eval_dataset = VOCDataset("/home/data/voc2007_2012/VOCdevkit/VOC2012",
resize_size=[800, 1024],
split='val2007')
print("INFO===>eval dataset has %d imgs" % len(eval_dataset))
eval_loader = torch.utils.data.DataLoader(
eval_dataset,
batch_size=1,
shuffle=False,
collate_fn=eval_dataset.collate_fn)
model = FCOSDetector(mode="inference")
# model=torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
# print("INFO===>success convert BN to SyncBN")
model.load_state_dict(
torch.load(
"./logs/voc20172012_multigpu_800x1024_epoch27_loss0.5987.pth",
map_location=torch.device('cpu')))
from model.fcos import FCOSDetector
import torch
from dataloader.VOC_dataset import VOCDataset
import math,time
from torch.utils.tensorboard import SummaryWriter
from model.config import DefaultConfig as cfg
train_dataset=VOCDataset(cfg.dataset_dir,resize_size=cfg.image_scale,split='train')
val_dataset=VOCDataset(cfg.dataset_dir,resize_size=[512,800],split='val')
model=FCOSDetector(mode="training").cuda()
if cfg.resume_path:
model.load_state_dict(torch.load(cfg.resume_path))
optimizer=torch.optim.Adam(model.parameters(),lr=1e-4)
BATCH_SIZE=cfg.BATCH_SIZE
EPOCHS=cfg.EPOCHS
WARMPUP_STEPS_RATIO=cfg.WARMPUP_STEPS_RATIO
train_loader=torch.utils.data.DataLoader(train_dataset,batch_size=BATCH_SIZE,shuffle=True,collate_fn=train_dataset.collate_fn)
val_loader=torch.utils.data.DataLoader(val_dataset,batch_size=BATCH_SIZE,shuffle=True,collate_fn=val_dataset.collate_fn)
steps_per_epoch=len(train_dataset)//BATCH_SIZE
TOTAL_STEPS=steps_per_epoch*EPOCHS
WARMPUP_STEPS=TOTAL_STEPS*WARMPUP_STEPS_RATIO
GLOBAL_STEPS=cfg.GLOBAL_STEPS
LR_INIT=cfg.LR_INIT
LR_END=cfg.LR_END
writer=SummaryWriter(log_dir="./logs")